---
license_link: https://www.apache.org/licenses/LICENSE-2.0
license: apache-2.0
language:
- en
- zh
pipeline_tag: text-generation
library_name: transformers
tags:
- WebWorld
- web-agent
- world-model
- simulator
- browser
- a11y
- html
- xml
- markdown
- long-horizon
- long-context
- synthetic-trajectories
- instruction-tuning
base_model_relation: finetune
base_model:
- Qwen/Qwen3-8B
datasets:
- Qwen/WebWorldData
---

# WebWorld 🌐

[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/LICENSE-2.0) 
[![GitHub](https://img.shields.io/badge/GitHub-WebWorld-4b32c3?logo=github)](https://github.com/QwenLM/WebWorld) 
[![Dataset](https://img.shields.io/badge/HF%20Dataset-WebWorldData-yellow?logo=huggingface)](https://huggingface.co/datasets/Qwen/WebWorldData) 
[![MS Dataset](https://img.shields.io/badge/ModelScope-Dataset-7B42BC)](https://modelscope.cn/datasets/Qwen/WebWorldData) 
[![8B](https://img.shields.io/badge/Model-8B-green?logo=huggingface)](https://huggingface.co/Qwen/WebWorld-8B) 
[![MS 8B](https://img.shields.io/badge/ModelScope-8B-7B42BC)](https://modelscope.cn/models/Qwen/WebWorld-8B) 
[![14B](https://img.shields.io/badge/Model-14B-green?logo=huggingface)](https://huggingface.co/Qwen/WebWorld-14B) 
[![MS 14B](https://img.shields.io/badge/ModelScope-14B-7B42BC)](https://modelscope.cn/models/Qwen/WebWorld-14B) 
[![32B](https://img.shields.io/badge/Model-32B-green?logo=huggingface)](https://huggingface.co/Qwen/WebWorld-32B) 
[![MS 32B](https://img.shields.io/badge/ModelScope-32B-7B42BC)](https://modelscope.cn/models/Qwen/WebWorld-32B)


## 📚 Introduction

**WebWorld** is a large-scale **open-web world model** series for training and evaluating web agents. It is trained on **1M+ real-world web interaction trajectories** via a scalable hierarchical data pipeline, supporting:

- **Long-horizon simulation** (30+ steps)
- **Multi-format state representations**: A11y Tree, HTML, XML, Markdown, and natural language
- **CoT-activated reasoning** for transition prediction
- **Cross-domain generalization** to code, GUI, and game environments

Agents trained on WebWorld-synthesized trajectories achieve **+9.9% on MiniWob++** and **+10.9% on WebArena**. When used for inference-time lookahead search, WebWorld **outperforms GPT-5** as a world model.

## 🎯 Model Series

| Model | Base Model | HuggingFace Link | ModelScope Link |
|---|---|---|---|
| **WebWorld-8B** | [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) | [🤗 HuggingFace](https://huggingface.co/Qwen/WebWorld-8B) | [🤖 ModelScope](https://modelscope.cn/models/Qwen/WebWorld-8B) |
| **WebWorld-14B** | [Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B) | [🤗 HuggingFace](https://huggingface.co/Qwen/WebWorld-14B) | [🤖 ModelScope](https://modelscope.cn/models/Qwen/WebWorld-14B) |
| **WebWorld-32B** | [Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) | [🤗 HuggingFace](https://huggingface.co/Qwen/WebWorld-32B) | [🤖 ModelScope](https://modelscope.cn/models/Qwen/WebWorld-32B) |

**WebWorldData**: [Huggingface: Qwen/WebWorldData](https://huggingface.co/datasets/Qwen/WebWorldData), [ModelScope: Qwen/WebWorldData](https://modelscope.cn/datasets/Qwen/WebWorldData)

💡 **Recommendation**: Use 8B for fast simulation and data synthesis; use 14B/32B for higher-fidelity simulation and better long-horizon robustness. For best results in a specific environment, we recommend task-specific fine-tuning on in-domain trajectories.

## 🛠️ Requirements

- `transformers` (recommended: latest version)
- `torch`
- Optional: `accelerate`, `vllm` for efficient serving

## 🚀 Quick Start

**Key Notes:**
- WebWorld predicts the next page state given the current state and an action.
- It strictly preserves the input/output format (A11y / HTML / XML / Markdown / NL).
- Supports multi-turn trajectory simulation up to 30+ steps.

### Single-Step Prediction

<details>
<summary>💻 Click to expand code</summary>

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "Qwen/WebWorld-8B"  # or WebWorld-14B, WebWorld-32B
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
).eval()

system_prompt = (
    "You are a web world model. I will provide you with an initial page state "
    "and a sequence of actions. For each action, predict the resulting page state.\n"
    "Strictly maintain the original format. Output only the full page state "
    "without explanations, code, or truncation."
)

current_state = """RootWebArea 'Global Start - Your Daily Portal', focused
\t[1] banner 'Top Header', visible
\t\t[2] link 'Set as Homepage', clickable, visible
\t\t[3] link 'Feedback', clickable, visible
\t\t[5] region 'Weather Widget', visible
\t\t\tStaticText 'New York, USA'
\t\t\t[6] image 'Sunny', visible
\t\t\tStaticText '24°C'
\t\t[8] link 'Sign In', clickable, visible
\t[10] region 'Search Area', visible
\t\t[11] image 'Global Start Logo', visible
\t\tStaticText 'Search the entire web'
\t\t[12] tablist 'Search Engine Selector', orientation='horizontal'
\t\t\t[13] tab 'Google', selected=True, clickable
\t\t\t[14] tab 'Bing', selected=False, clickable
\t\t\t[15] tab 'DuckDuckGo', selected=False, clickable
\t\t[18] combobox 'Web Search', clickable, visible, autocomplete='both', expanded=False
\t\t\t[19] textbox 'Type keywords or URL...', clickable, visible, editable, value=''
\t\t[20] button 'Search', clickable, visible
\t[30] navigation 'Category Bar', visible
\t\t[31] link 'Home', clickable, selected=True
\t\t[32] link 'News', clickable
\t\t[33] link 'Video', clickable
\t\t[34] link 'Shopping', clickable
\t\t[35] link 'Social', clickable
\t[50] main 'Site Directory', visible
\t\t[51] region 'Top Recommended', visible
\t\t\t[52] heading 'Most Popular', visible
\t\t\t[53] list 'Top Sites Grid', visible
\t\t\t\t[54] link 'Facebook', clickable
\t\t\t\t[56] link 'YouTube', clickable
\t\t\t\t[58] link 'Amazon', clickable
\t\t\t\t[60] link 'Twitter / X', clickable
\t\t\t\t[62] link 'Instagram', clickable
\t\t\t\t[64] link 'Wikipedia', clickable
\t\t\t\t[66] link 'Netflix', clickable
\t\t\t\t[68] link 'LinkedIn', clickable
\t\t[80] region 'News & Media', visible
\t\t\t[81] heading 'Latest News', visible
\t\t\t[82] link 'CNN', clickable
\t\t\t[83] link 'BBC', clickable
\t\t\t[84] link 'The Verge', clickable
\t\t[90] region 'Shopping', visible
\t\t\t[91] heading 'E-Commerce', visible
\t\t\t[92] link 'eBay', clickable
\t\t\t[93] link 'Walmart', clickable
\t\t\t[94] link 'Best Buy', clickable
\t[200] complementary 'Ads', visible
\t\t[201] image 'Ad: Travel to Japan'
\t\t[202] link 'Book Now', clickable
\t[300] contentinfo 'Footer', visible
\t\tStaticText '© 2026 Global Start Inc.'"""

user_message = (
    f"Initial Page State:\n{current_state}\n\n"
    f"First Action: 'click([32])'\n\n"
    f"Next Page State:"
)

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": user_message},
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=4096,
        do_sample=False,
    )

response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
print(response)
```

</details>

### Multi-Turn Simulation

The first turn provides the initial state and first action. Each subsequent turn uses a fixed continuation prompt:

<details>
<summary>💻 Click to expand code</summary>

```python
CONTINUE_PROMPT = (
    "Continue the trajectory. Given the previous state, "
    "predict the next page state after this action.\n\n"
    "Action: '{action}'\n\nNext Page State:"
)

# Turn 1
messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": f"Initial Page State:\n{state_0}\n\nFirst Action: '{action_0}'\n\nNext Page State:"},
]
state_1 = generate(messages)  # your generate function

# Turn 2
messages.append({"role": "assistant", "content": state_1})
messages.append({"role": "user", "content": CONTINUE_PROMPT.format(action=action_1)})
state_2 = generate(messages)

# Turn 3, 4, ... up to 30+ turns: repeat the same pattern
messages.append({"role": "assistant", "content": state_2})
messages.append({"role": "user", "content": CONTINUE_PROMPT.format(action=action_2)})
state_3 = generate(messages)
```

</details>

## 🎮 Action Space

WebWorld supports a unified action space as Python-style function calls:

| Category | Action | Description |
|---|---|---|
| **Element** | `click(bid, button, modifiers)` | Click a DOM element by its ID |
| | `fill(bid, text, press_enter)` | Type text into an input field |
| | `select_option(bid, options)` | Select from a dropdown / combobox |
| | `hover(bid)` | Hover over an element |
| **Mouse** | `mouse_move(x, y)` | Move cursor to coordinates |
| | `mouse_click(x, y, button)` | Click at coordinates |
| | `mouse_down(x, y)` / `mouse_up(x, y)` | Press / release (drag-and-drop) |
| **Keyboard** | `keyboard_press(key)` | Press a key (e.g., `Enter`, `Tab`) |
| | `keyboard_type(text)` | Type a string sequentially |
| **Browser** | `scroll(dx, dy)` | Scroll the viewport |
| | `goto(url)` | Navigate to a URL |
| | `go_back()` / `go_forward()` | Browser history navigation |
| | `tab_new()` / `tab_close()` / `tab_focus(index)` | Manage browser tabs |
| **Meta** | `send_msg_to_user(text)` | Send a message to the user |
| | `noop(wait_ms)` | Wait for a duration |
| | `infeasible(reason)` | Declare the task impossible |

## 📊 Performance

### Intrinsic Evaluation (WebWorld-Bench)

WebWorld-Bench evaluates models using **Factuality Score** (functional correctness) and **Web Turing Score** (perceptual realism) across nine dimensions:

| Model | Avg Factuality | Avg Turing |
|---|---|---|
| GPT-4o | 59.5 | 35.4 |
| Claude-Opus-4.1 | **71.3** | **47.4** |
| Gemini-3-Pro | 70.3 | 43.2 |
| Qwen3-8B (base) | 26.9 | 17.4 |
| **WebWorld-8B** | **70.1** | **42.2** |
| **WebWorld-14B** | 70.7 | 44.7 |
| **WebWorld-32B** | **71.0** | **45.6** |

### Extrinsic Evaluation (Agent Training)

| Model | MiniWob++ SR | WebArena SR |
|---|---|---|
| GPT-4o | 64.3% | 26.6% |
| Qwen3-8B (base) | 49.4% | 9.8% |
| **Qwen3-8B + WebWorld** | **59.3%** (+9.9%) | **20.7%** (+10.9%) |
| Qwen3-14B (base) | 54.9% | 15.1% |
| **Qwen3-14B + WebWorld** | **63.2%** (+8.3%) | **24.3%** (+9.2%) |

### Cross-Domain Generalization

| Environment | Qwen3-8B | WebWorld-8B | Gain |
|---|---|---|---|
| API Services | 0.088 | **0.299** | +0.211 |
| Code | 0.147 | **0.396** | +0.249 |
| Game | 0.253 | **0.473** | +0.220 |
| GUI Desktop | 0.322 | **0.705** | +0.383 |

## ⚠️ Limitations

- **Sycophancy / optimism bias**: the model may generate outcomes that are overly favorable to the agent's intended action.
- **Content generation fidelity**: long-form, high-precision content (e.g., scientific articles) is not the primary target.
- **Text-only**: WebWorld does not simulate visual / pixel-level rendering.

## 📝 Citation

```bibtex
@misc{xiao2026webworldlargescaleworldmodel,
      title={WebWorld: A Large-Scale World Model for Web Agent Training}, 
      author={Zikai Xiao and Jianhong Tu and Chuhang Zou and Yuxin Zuo and Zhi Li and Peng Wang and Bowen Yu and Fei Huang and Junyang Lin and Zuozhu Liu},
      year={2026},
      eprint={2602.14721},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2602.14721}, 
}